Maximum IOPS for a 10K or 15K SAS Hard Disk Drive is not 170! 2

At times, mmm quite often now actually (perhaps it’s because I’m getting older and more grumpy) the industry I’m part of really makes me embarrassed, all too often folk just quote things they’ve seen on Wikipedia or via word of mouth without even a basic knowledge of the “fact” they are quoting – a “fact”(…)

The inescapable problem of Latency within Data-based systems 2

It’s long troubled me people touting Real-Time Business Intelligence when its physically impossible to achieve true real-time; I’ve jotted down some thoughts in an attempt to bring order and better understanding to the reality of “Real-Time” – hopefully the post will help you in dealing with vendors, giving you some insight into what questions to(…)

SQL Server Hekaton (XTP) in-memory tables: Choosing the correct BUCKET_COUNT for a Hash Index 2

In this post I cover off how to choose the correct number for the BUCKET_COUNT and how you go about that, also, how to monitor and change the bucket_count. The general approach is that you set the BUCKET_COUNT to the number of unique values there will be given just the columns on your hash index (see(…)

SQL Server Hekaton (XTP) in-memory Tables: Range Indexes and Row Chains 2

Hash and Range indexes both involve row chains, if you haven’t already read my post on Understanding the row chains of Hash Indexes I’d suggest you do before continuing with this post which essentially is a continuation of it and assumes you know the basics of row chains already. A range index is implemented using the(…)

SQL Server Hekaton In-memory tables: Understanding the Row Chains of Hash Indexes 5

Having a good understanding of how the hashing and row chains work will go a long way in helping you design for performance and diagnose performance and resource issues you may get once live. This post covers off some of the basics and hopefully will give you a working insight. We’ll start with a Hash(…)

Throughput improvement through Delayed Durability on COMMIT TRAN from SQL Server 2014 2

Durability is not a requirement of a relational database, you would term a database system as ACID compliant where the D in ACID is Durability, note – HBASE which sit’s upon HADOOP is ACID compliant! ACID applies to Transactions and not the prevailing database organisation method e.g. Relational, Key Value, Hierarchical etc. Back to SQL(…)

Hekaton In-Memory Tables: HASH Indexes 1

The purpose of this post is to help you understand the new HASH indexing in SQL Server 2014 in-memory tables feature (project Hekaton). As ever, it’s actually quite a big topic so I’ll cover aspects in multiple posts (I’ll pop back here and update the links once complete)… Post 1 – How the Hash Index works Post(…)

What is Hashing; using Modulus to partition data 3

Hopefully this post goes some way in helping the reader understand better what hashing, hash indexes are and the need for row chains with In-memory Tables (Hekaton) in SQL Server 2014 hash indexes. Purpose of hashing? Hashing can be used to index character data, instead of building an index on a varchar(50) column for example,(…)

Creating a Uniform, Normal and Benford Law’s Distribution from Random Numbers in SQL Server 0

Creating test data we often utilise random numbers, within SQL Server we can use the RAND() function or NEWID(). This quick post shows you how to create three different distributions based on the set {1..9} – Uniform (evenly distributed), Normal (distributed about the mean) and Benford – distribution follows Benfords Law of Log10( 1 +(…)

Reducing SQL Server IO and Access Times using Bloom Filters – Part 3 (Inserting Data) 2

Part 2 (Basics of the method in SQL Server) explained how to get data into a Bloom Filter structure, it now needs persisting. This post explains a method on how a Bloom Filter can be stored in a SQL Server database – I assume you have read Part 1 and Part 2 and understand about(…)

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